Biology subjects

Saykin, A. J.

Publications and source records attributed to Saykin, A. J..

At least 19 recordsLinked to original sources

bioRxiv2026

Continuous amyloid staging complements global amyloid burden in early Alzheimer's disease

Introduction: Early Alzheimer's disease (AD) staging remains challenging because clinical categories and global amyloid PET measures provide only coarse characterization of disease progression. We developed a continuous amyloid staging score from regional amyloid PET to complement global amyloid burden. Methods: Using longitudinal florbetapir PET from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we trained an end-to-end deep learning model to directly estimate a continuous amyloid staging score. We evaluated longitudinal consistency, its relationship with global amyloid burden, regional contributors to early-stage variation, and whether the staging score stratified subsequent memory decline among participants matched for baseline global amyloid burden. Cognitive analyses were replicated in A4+LEARN. Results: The staging score better preserved longitudinal visit order than existing amyloid staging approaches. During early disease stages, it captured regional amyloid variation despite relatively stable global amyloid burden. Among participants with similar baseline global amyloid burden, higher staging scores were associated with faster subsequent memory decline in both ADNI and A4+LEARN. Discussion: A continuous amyloid staging score complements global amyloid burden for early AD staging and cognitive risk stratification. Keywords: Alzheimer's disease; amyloid PET; disease staging; cognitive decline

neuroscience↗

bioRxiv2026

Cross-Regional Trajectory Analysis Reveals Distinct Temporal Progression of Shared Proteomic Programs in Alzheimer's Disease

Alzheimer's pathology progresses through anatomically distinct brain regions, yet how molecular programs are temporally organized across vulnerable regions remains poorly understood. Here, we reconstructed continuous proteomic trajectories from matched dorsolateral prefrontal cortex (DLPFC) and superior temporal gyrus (STG) proteomes to investigate cross-regional molecular progression in AD. The inferred trajectories closely recapitulated established neuropathological staging while remaining independent of age, sex, and race. Across regions, a mitochondrial bioenergetic protein subset declined substantially earlier in the STG than in the DLPFC, revealing a previously unrecognized regional temporal offset in metabolic dysfunction. In parallel, a broadly shared proteomic program progressed in both regions but more rapidly in the STG, indicating that shared molecular responses differ in their temporal progression despite overall coordination. Together, these findings demonstrate that regional vulnerability in AD is reflected not only by the molecular programs involved but also by their temporal organization during disease progression.

neuroscience↗

bioRxiv2026

Gene-Modulated Network Diffusion for Improved Modeling of Amyloid-β Spread in Alzheimer's Disease

Understanding the pathogenesis of amyloid-{beta} pathology in Alzheimers Disease (AD) proves to be a challenge. In this work, we expand upon the application of network diffusion models (NDM) to study pathophysiological spread of amyloid-{beta} throughout white matter structural brain networks. We found that the NDM successfully recaptures subpopulation-level spatial patterns (Pearsons R=0.45-0.48, PFDR < 0.01) of amyloid-{beta} deposition in the Alzheimers Disease Neuroimaging Cohort at a regional level, but with drawbacks in mechanism interpretability. We then moved to an extended NDM framework (eNDM), including a protein synthesis term to better reflect the role of amyloid-{beta} metabolism, as well as including regional vulnerability using spatial transcriptomics from the Allen Human Brain Atlas to modulate the region-level rate parameters of the synthesis term. The novel gene eNDMs exhibited significant performance increases in Pearsons correlation (Steigers Z, PFDR < 0.10) over baseline NDM performance in mild cognitive impairment and AD groups using APOE, SORL1, and FGL2 for gene modulation. The results were robust and replicable when testing on an external cohort of the Alzheimers Disease Sequencing Project. The study thus demonstrates the importance of regional genetic vulnerability, in conjunction with network diffusion mechanisms, in improving the modelling and prediction of amyloid-{beta} pathophysiological spread.

bioinformatics↗

bioRxiv2026

Learning a Continuous Progression Trajectory of Amyloid in Alzheimer's disease

BACKGROUNDUnderstanding of early Alzheimer progression is critical for timely diagnosis and treatment evaluation, but traditional diagnostic groups often lack sensitivity to subtle early-stage changes. METHODSWe developed SLOPE, an unsupervised dimensionality reduction method that models the amyloid progression in AD on a continuous scale which preserves the temporal sequence of follow-up visits. Applied to longitudinal amyloid PET data, SLOPE generated a two-dimensional trajectory capturing global amyloid accumulation across the AD continuum. RESULTSSLOPE-derived pseudotime scores better preserved temporal consistency across diagnostic groups and longitudinal follow-up visits and can be generalized to held-out subjects. The learned trajectory revealed biologically consistent amyloid spreading patterns and greater sensitivity to early progression than global amyloid SUVR. DISCUSSIONSLOPE provides a continuous staging of amyloid pathology that complements global amyloid measures by capturing early localized progression.

bioinformatics↗

bioRxiv2024

Cell Type Differentiation Using Network Clustering Algorithms

Single cell RNA-seq (scRNA-seq) technologies provide unprecedented resolution representing transcriptomics at the level of single cell. One of the biggest challenges in scRNA-seq data analysis is the cell type annotation, which is usually inferred by cell separation approaches. In-silico algorithms that accurately identify individual cell types in ongoing single-cell sequencing studies are crucial for unlocking cellular heterogeneity and understanding the biological basis of diseases. In this study, we focus on robustly identifying cell types in single-cell RNA sequencing data; we conduct a comparative analysis using methods established in biology, like Seurat, Leiden, and WGCNA, as well as Infomap, statistical inference via Stochastic Block Models (SBM), and single-cell Graph Neural Networks (scGNN). We also analyze preprocessing pipelines to identify and optimize key components in the process. Leveraging two independent datasets, PBMC and ROSMAP, we employ clustering algorithms on cell-cell networks derived from gene expression data. Our findings reveal that while clusters detected by WGCNA exhibit limited correspondence with cell types, those identified by multiresolution Infomap and Leiden, and SBM show a closer alignment, with Infomap standing out as a particularly effective approach. Infomap notably offers valuable insights for the precise characterization of cellular landscapes related to neurodegenration and immunology in scRNA-seq.

bioinformatics↗

bioRxiv2023

Leveraging longitudinal diffusion MRI data to quantify differences in white matter microstructural decline in normal and abnormal aging

AbstractO_ST_ABSINTRODUCTIONC_ST_ABSIt is unclear how rates of white matter microstructural decline differ between normal aging and abnormal aging. METHODSDiffusion MRI data from several well-established longitudinal cohorts of aging [Alzheimers Neuroimaging Initiative (ADNI), Baltimore Longitudinal Study of Aging (BLSA), Vanderbilt Memory & Aging Project (VMAP)] was free-water corrected and harmonized. This dataset included 1,723 participants (age at baseline: 72.8{+/-}8.87 years, 49.5% male) and 4,605 imaging sessions (follow-up time: 2.97{+/-}2.09 years, follow-up range: 1-13 years, mean number of visits: 4.42{+/-}1.98). Differences in white matter microstructural decline in normal and abnormal agers was assessed. RESULTSWhile we found global decline in white matter in normal/abnormal aging, we found that several white matter tracts (e.g., cingulum bundle) were vulnerable to abnormal aging. CONCLUSIONSThere is a prevalent role of white matter microstructural decline in aging, and future large-scale studies in this area may further refine our understanding of the underlying neurodegenerative processes. HIGHLIGHTSO_LILongitudinal data was free-water corrected and harmonized C_LIO_LIGlobal effects of white matter decline were seen in normal and abnormal aging C_LIO_LIThe free-water metric was most vulnerable to abnormal aging C_LIO_LICingulum free-water was the most vulnerable to abnormal aging C_LI

neuroscience↗

bioRxiv2022

Genetic, clinical underpinnings of subtle early brain change along Alzheimer's dimensions

Alzheimers disease (AD) is associated with heterogeneous atrophy patterns. We employed a semi-supervised clustering technique known as Surreal-GAN, through which we identified two dominant dimensions of brain atrophy in symptomatic mild cognitive impairment (MCI) and AD patients: the "diffuse-AD" (R1) dimension shows widespread brain atrophy, and the "MTL-AD" (R2) dimension displays focal medial temporal lobe (MTL) atrophy. Critically, only R2 was associated with widely known sporadic AD genetic risk factors (e.g., APOE {varepsilon}4) in MCI and AD patients at baseline. We then independently detected the presence of the two dimensions in the early stages by deploying the trained model in the general population and two cognitively unimpaired cohorts of asymptomatic participants. In the general population, genome-wide association studies found 77 genes unrelated to APOE differentially associated with R1 and R2. Functional analyses revealed that these genes were overrepresented in differentially expressed gene sets in organs beyond the brain (R1 and R2), including the heart (R1) and the pituitary gland, muscle, and kidney (R2). These genes were enriched in biological pathways implicated in dendritic cells (R2), macrophage functions (R1), and cancer (R1 and R2). Several of them were "druggable genes" for cancer (R1), inflammation (R1), cardiovascular diseases (R1), and diseases of the nervous system (R2). The longitudinal progression showed that APOE {varepsilon}4, amyloid, and tau were associated with R2 at early asymptomatic stages, but this longitudinal association occurs only at late symptomatic stages in R1. Our findings deepen our understanding of the multifaceted pathogenesis of AD beyond the brain. In early asymptomatic stages, the two dimensions are associated with diverse pathological mechanisms, including cardiovascular diseases, inflammation, and hormonal dysfunction - driven by genes different from APOE - which may collectively contribute to the early pathogenesis of AD.

bioengineering↗

bioRxiv2022

Deep multi-omic network fusion for marker discovery of Alzheimer's Disease

Multi-omic data spanning from genotype, gene expression to protein expression have been increasingly explored to interpret findings from genome wide association studies of Alzheimers disease (AD) and to gain more insight of the disease mechanism. However, each -omics data type is usually examined individually and the functional interactions between genetic variations, genes and proteins are only used after discovery to interpret the findings, but not beforehand. In this case, multi-omic findings are likely not functionally related and therefore give rise to challenges in interpretation. To address this problem, we propose a new interpretable deep neural network model MoFNet to jointly model the prior knowledge of functional interactions and multi-omic data set. It aims to identify a subnetwork of functional interactions predictive of AD evidenced by multi-omic measures. Particularly, prior functional interaction network was embedded into the architecture of MoFNet in a way that it resembles the information flow from DNA to gene and protein. The proposed model MoFNet significantly outperformed all other state-of-art classifiers when evaluated using multi-omic data from the ROS/MAP cohort. Instead of individual markers, MoFNet yielded multi-omic sub-networks related to innate immune system, clearance of misfolded proteins, and neurotransmitter release respectively. Around 50% of these findings were replicated in another independent cohort. Our identified gene/proteins are highly related to synaptic vesicle function. Altered regulation or expression of these genes/proteins could cause disruption in neuron-neuron or neuron-glia cross talk and further lead to neuronal and synapse loss in AD. Further investigation of these identified genes/proteins could possibly help decipher the mechanisms underlying synaptic dysfunction in AD, and ultimately inform therapeutic strategies to modify AD progression in the early stage.

systems biology↗

bioRxiv2021

Single Nuclei Transcriptome Reveals Perturbed Brain Vascular Molecules in Alzheimer's Disease

Blood-brain barrier (BBB) dysfunction is well-known in Alzheimers disease (AD), but the precise molecular changes contributing to its pathophysiology are unclear. To understand the transcriptional changes in brain vascular cells, we performed single nucleus RNA sequencing (snRNAseq) of temporal cortex tissue in 24 AD and control brains resulting in 79,751 nuclei, 4,604 of which formed three distinct vascular clusters characterized as activated pericytes, endothelia and resting pericytes. We identified differentially expressed genes (DEGs) and their enriched pathways in these clusters and detected the most transcriptional changes within activated pericytes. Using our data and a knowledge-based predictive algorithm, we discovered and prioritized molecular interactions between vascular and astrocyte clusters, the main cell types of the gliovascular unit (GVU) of the BBB. Vascular targets predicted to interact with astrocytic ligands have biological functions in signalling, angiogenesis, amyloid {beta} metabolism and cytoskeletal structure. Top astrocytic and vascular interacting molecules include both novel and known AD risk genes such as APOE, APP and ECE1. Our findings provide information on transcriptional changes in predicted vascular-astrocytic partners at the GVU, bringing insights to the molecular mechanisms of BBB breakdown in AD. Graphical AbstractPericytes (yellow), endothelia (salmon) and astrocytes (purple) that form the gliovascular unit (GVU) at the blood brain barrier (BBB) were interrogated for their differentially expressed genes (DEG) and vascular cell (pericyte or endothelia) to astrocyte interactions using single nucleus RNA sequencing (RNAseq) transcriptome obtained from brains of Alzheimers disease (AD) patients and controls. We identified many upregulated (red) or downregulated (blue) DEGs in AD brains in these cell types. These genes have known biological functions in amyloid {beta} (A{beta}) clearance, immune modulation, astrogliosis and neuronal death. Novel predicted interactions were identified between vascular cells and astrocytic DEGs. Collectively, our findings highlight the vast transcriptome changes that occur at the GVU and provide mechanistic insights into BBB dysfunction in AD. This figure was created with Biorender.com. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=144 SRC="FIGDIR/small/474255v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@f48748org.highwire.dtl.DTLVardef@1ddc3f4org.highwire.dtl.DTLVardef@6bfa5eorg.highwire.dtl.DTLVardef@1024506_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2020

Serum Metabolites Associate with Brain Amyloid Beta Deposition, Cognitive Dysfunction, and Alzheimers Disease Progression

RATIONALEMetabolomics in the Alzheimers Disease Neuroimaging Initiative (ADNI) cohort provides a powerful tool for mapping biochemical changes in AD, and a unique opportunity to learn about the association between circulating blood metabolites and brain amyloid-{beta} deposition in AD. OBJECTIVESWe examined 140 serum metabolites and their associations with brain amyloid-{beta} deposition, cognition, and conversion from mild cognitive impairment (MCI) to AD. FINDINGSSerum-based targeted metabolite levels were measured in 1,531 ADNI participants. We performed association analysis of metabolites with brain amyloid-{beta} deposition measured from [18F] Florbetapir PET scans. We identified nine metabolites as significantly associated with amyloid-{beta} deposition after FDR-based multiple comparison correction. Higher levels of one acylcarnitine (C3; propionylcarnitine) and one biogenic amine (kynurenine) were associated with decreased amyloid-{beta} accumulation. However, higher levels of seven phosphatidylcholines (PC) were associated with increased amyloid deposition. In addition, PC ae C44:4 was significantly associated with cognition and conversion from MCI to AD dementia. CONCLUSIONPerturbations in PC and acylcarnitine metabolism may play a role in features intrinsic to AD including amyloid-{beta} deposition and cognitive performance.

neuroscience↗

bioRxiv2020

WEVar: a novel statistical learning framework for predicting noncoding regulatory variants

Understanding the functional consequence of noncoding variants is of great interest. Though genome-wide association studies (GWAS) or quantitative trait locus (QTL) analyses have identified variants associated with traits or molecular phenotypes, most of them are located in the noncoding regions, making the identification of causal variants a particular challenge. Existing computational approaches developed for for prioritizing non-coding variants produce inconsistent and even conflicting results. To address these challenges, we propose a novel statistical learning framework, which directly integrates the precomputed functional scores from representative scoring methods. It will maximize the usage of integrated methods by automatically learning the relative contribution of each method and produce an ensemble score as the final prediction. The framework consists of two modes. The first "context-free" mode is trained using curated causal regulatory variants from a wide range of context and is applicable to predict noncoding variants of unknown and diverse context. The second "context-dependent" mode further improves the prediction when the training and testing variants are from the same context. By evaluating the framework via both simulation and empirical studies, we demonstrate that it outperforms integrated scoring methods and the ensemble score successfully prioritizes experimentally validated regulatory variants in multiple risk loci.

bioinformatics↗

bioRxiv2020

Differential brain atrophy and neurogenetic profiles in cognitively-defined Alzheimer's disease subgroups

Elucidating mechanisms underlying the clinical heterogeneity observed among individuals with Alzheimers disease (AD) is key to facilitate personalized treatments. We categorized 679 individuals with AD into subgroups based on a relative impairment in one cognitive domain (i.e. AD-Memory, AD-Executive-Functioning, AD-Language and AD-Visuospatial-Functioning). We compared atrophy patterns derived from MRI and identified patterns that closely matched the respective cognitive profiles, i.e. medial temporal lobe atrophy in AD-Memory, fronto-parietal in AD-Executive-Functioning, asymmetric left-temporal in AD-Language, and posterior in AD-Visuospatial-Functioning. We then determined spatial correlations between subgroup-specific atrophy and a transcriptomic atlas of gene expression, which revealed both shared (e.g. mitochondrial respiration and synaptic function/plasticity) and subgroup-specific (e.g. cell-cycle for AD-Memory, protein metabolism in AD-Language, and modification of gene expression in AD-Visuospatial-Functioning) biological pathways associated with each subgroups atrophy patterns. We conclude that cognitive heterogeneity in AD is related to neuroanatomical differences, and specific biological pathways may be involved in their emergence.

neuroscience↗

bioRxiv2020

Deep learning detection of informative features in tau PET for Alzheimer' s disease classification

BackgroundAlzheimers disease (AD) is the most common type of dementia, typically characterized by memory loss followed by progressive cognitive decline and functional impairment. Many clinical trials of potential therapies for AD have failed, and there is currently no approved disease-modifying treatment. Biomarkers for early detection and mechanistic understanding of disease course are critical for drug development and clinical trials. Amyloid has been the focus of most biomarker research. Here, we developed a deep learning-based framework to identify informative features for AD classification using tau positron emission tomography (PET) scans. MethodsWe analysed [18F]flortaucipir PET image data from the Alzheimers Disease Neuroimaging Initiative (ADNI) cohort. We first developed an image classifier to distinguish AD from cognitively normal (CN) older adults by training a 3D convolutional neural network (CNN)-based deep learning model on tau PET images (N=132; 66 CN and 66 AD), then applied the classifier to images from individuals with mild cognitive impairment (MCI; N=168). In addition, we applied a layer-wise relevance propagation (LRP)-based model to identify informative features and to visualize classification results. We compared these results with those from whole brain voxel-wise between-group analysis using conventional Statistical Parametric Mapping (SPM12). ResultsThe 3D CNN-based classification model of AD from CN yielded an average accuracy of 90.8% based on five-fold cross-validation. The LRP model identified the brain regions in tau PET images that contributed most to the AD classification from CN. The top identified regions included the hippocampus, parahippocampus, thalamus, and fusiform. The LRP results were consistent with those from the voxel-wise analysis in SPM12, showing significant focal AD associated regional tau deposition in the bilateral temporal lobes including the entorhinal cortex. The AD probability scores calculated by the classifier were correlated with brain tau deposition in the medial temporal lobe in MCI participants (r=0.43 for early MCI and r=0.49 for late MCI). ConclusionA deep learning framework combining 3D CNN and LRP algorithms can be used with tau PET images to identify informative features for AD classification and may have application for early detection during prodromal stages of AD.

neuroscience↗

bioRxiv2020

Elevated plasma cortisol associated with larger ventricles and smaller hippocampal volumes, a study in 2 independent elderly cohorts

Cortisol is considered the most fundamental stress hormone and is elevated in stress and multiple neuropsychiatric conditions. Prior studies have shown associations of plasma cortisol levels with total cerebral and hippocampal volumes and less consistently with the amygdala. Here, we extend our hypothesis to test associations of plasma cortisol with 1) ventricular 2) hippocampal and 3) amygdalar volumes, in two independent elderly cohorts across a broad cognitive spectrum ranging from normal cognition to Alzheimers disease. We demonstrate elevated cortisol to be associated with larger lateral ventricular volumes and smaller hippocampal volumes, predominantly in the right cerebral hemisphere, regardless of age, sex or cognitive status. We noted a non-significant trend of smaller amygdalar volumes with elevated cortisol. Our findings support smaller brain parenchyma volumes seen with elevated cortisol and may encourage effective strategies reducing cortisol and stress. They may also serve as imaging biomarkers for assessing therapeutic benefits of stress and cortisol lowering interventions aiming to halt or reverse the brain volume alterations and theoretically improve cognition and quality of life. HighlightsO_LIElevated cortisol associated with larger ventricular volumes and smaller hippocampal volumes C_LIO_LIAssociations are predominantly noted in the right cerebral hemisphere. C_LIO_LISimilar non-significant trends noted in amygdalar volumes C_LIO_LICortisol and stress reducing strategies may halt brain changes and improve quality of life C_LIO_LIImaging biomarkers may help assess efficacy of cortisol-lowering therapeutic interventions C_LI

neuroscience↗

bioRxiv2020

Functional biological paths altered in Alzheimer's disease: from genes to bile acids

Large-scale genome wide association studies (GWASs) have been performed in search for risk genes for Alzheimers disease (AD). Despite the significant progress, replicability of genetic findings and their translation into targetable mechanisms related to the disease pathogenesis remains a challenge. Given that bile acids have been suggested in recent metabolic studies as potential age-related metabolic factors associated with AD, we integrated genomic and metabolomic data together with heterogeneous biological networks and investigated the potential cascade of effect of genetic variations to proteins, bile acids and ultimately AD brain phenotypes. Particularly, we leveraged functional protein interaction networks and metabolic networks and focused on the genes directly interacting with AD-altered bile acids and their functional regulators. We examined the association of all the SNPs located in those candidate genes with AD brain imaging phenotypes, and identified multiple AD risk SNPs whose downstream genes and bile acids were also found to be altered in AD. These AD related markers span from genetics to metabolomics, forming functional biological paths connecting across multiple-omics layers, and give valuable insights into the underlying mechanism of AD.

bioinformatics↗

bioRxiv2019

A/T/N polygenic risk score for cognitive decline in old age

INTRODUCTIONWe developed a novel polygenic risk score (PRS) based on the A/T/N (amyloid plaques (A), phosphorylated tau tangles (T), and neurodegeneration (N)) framework and compared a PRS based on clinical AD diagnosis to assess which was a better predictor of cognitive decline. METHODSWe used summary statistics from genome wide association studies of cerebrospinal fluid amyloid-{beta} (A{beta}42) and phosphorylated-tau (ptau181), left hippocampal volume (LHIPV), and late-onset AD dementia to calculate PRS for 1181 participants in the Alzheimers Disease Neuroimaging Initiative (ADNI). Individual PRS were averaged to generate a composite A/T/N PRS. We assessed the association of PRS with baseline and longitudinal cognitive composites of executive function and memory. RESULTSThe A/T/N PRS showed superior predictive performance on AD biomarkers and executive function decline compared to the clinical AD PRS. DISCUSSIONResults suggest that integration of genetic risk across AD biomarkers may improve prediction of disease progression. Research in ContextO_ST_ABSSystematic ReviewC_ST_ABSAuthors reviewed relevant literature using PubMed and Google Scholar. Key studies that generated and validated polygenic risk scores (PRS) for clinical and pathologic AD were cited. PRS scores have been increasingly used in the literature but clinical utility continues to be questioned. InterpretationIn the current research landscape concerning PRS clinical utility in the AD space, there is room for model improvement and our hypothesis was that a PRS with integrated risk for AD biomarkers could yield a better model for cognitive decline. Future DirectionsThis study serves as proof-of-concept that encourages future study of integrated PRS across disease markers and utility in taking an A/T/N (amyloidosis, tauopathy and neurodegeneration) focused approach to genetic risk for cognitive decline and AD.

genomics↗

bioRxiv2019

Identifying differences in bile acid pathways for cholesterol clearance in Alzheimer’s disease using metabolic networks of human brain regions

Alzheimers disease (AD) is the leading cause of dementia, with metabolic dysfunction seen years before the emergence of clinical symptoms. Increasing evidence suggests a role for primary and secondary bile acids, the end-product of cholesterol metabolism, influencing pathophysiology in AD. In this study, we analyzed transcriptomes from 2114 post-mortem brain samples from three independent cohorts and identified that the genes involved in alternative bile acid synthesis pathway was expressed in brain compared to the classical pathway. These results were supported by targeted metabolomic analysis of primary and secondary bile acids measured from post-mortem brain samples of 111 individuals. We reconstructed brain region-specific metabolic networks using data from three independent cohorts to assess the role of bile acid metabolism in AD pathophysiology. Our metabolic network analysis suggested that taurine transport, bile acid synthesis and cholesterol metabolism differed in AD and cognitively normal individuals. Using the brain transcriptional regulatory network, we identified putative transcription factors regulating these metabolic genes and influencing altered metabolism in AD. Intriguingly, we find bile acids from the brain metabolomics whose synthesis cannot be explained by enzymes we find in the brain, suggesting they may originate from an external source such as the gut microbiome. These findings motivate further research into bile acid metabolism and transport in AD to elucidate their possible connection to cognitive decline.

systems biology↗

bioRxiv2019

The Alzheimer’s Disease Metabolome: Effects of Sex and APOE ε4 genotype

Recent studies have provided evidence that late-onset Alzheimers disease (AD) can in part be considered a metabolic disease. Besides age, female sex and APOE {varepsilon}4 genotype represent strong risk factors for AD. They also both give rise to large metabolic differences, suggesting that metabolic aspects of AD pathogenesis may differ between males and females and between APOE {varepsilon}4 carriers and non-carriers. We systematically investigated group-specific metabolic alterations by conducting stratified association analyses of 140 metabolites measured in serum samples of 1,517 AD neuroimaging initiative subjects, with AD biomarkers for A{beta} and tau pathology and neurodegeneration. We observed substantial sex differences in effects of 15 metabolites on AD biomarkers with partially overlapping differences for APOE {varepsilon}4 status groups. These metabolites highlighted several group-specific alterations not observed in unstratified analyses using sex and APOE {varepsilon}4 as covariates. Combined stratification by both variables uncovered further subgroup-specific metabolic effects limited to the group with presumably the highest AD risk: APOE {varepsilon}4+ females. Pathways linked to the observed metabolic alterations suggest that females experience more expressed impairment of mitochondrial energy production in AD than males. These findings indicate that dissecting metabolic heterogeneity in AD pathogenesis may enable grading of the biomedical relevance of specific pathways for specific subgroups. Extending our approach beyond simple one- or two-fold stratification may thus guide the way to personalized medicine.\n\nSignificance statementResearch provides substantial evidence that late-onset Alzheimers disease (AD) is a metabolic disease. Besides age, female sex and APOE{varepsilon}4 genotype represent strong risk factors for AD, and at the same time give rise to large metabolic differences. Our systematic investigation of sex and APOE {varepsilon}4 genotype differences in the link between metabolism and measures of pre-symptomatic AD using stratified analysis revealed several group-specific metabolic alterations that were not observed without sex and genotype stratification of the same cohort. Pathways linked to the observed metabolic alterations suggest females are more affected by impairment of mitochondrial energy production in AD than males, highlighting the importance of tailored treatment approaches towards a precision medicine approach.

bioinformatics↗